Alfred Gangl

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The diagnosis of colorectal cancer is usually supported by a staging system, such as the Duke or TNM system. In this work we discuss computer-aided pit-pattern classification of surface structures observed during high-magnification colonoscopy in order to support dignity assessment of colonic polyps. This is considered a quite promising approach because it(More)
This paper describes an application of machine learning techniques and evolutionary algorithms to colon cancer diagnosis. We propose an automated classification system for endoscopical images, which is supposed to support physicians in making correct decisions. Classification is done according to the pit-pattern scheme, which defines two/six different(More)
In this paper, we present a novel approach to predict the histological diagnosis of colorectal lesions from high-magnification colonoscopy images by means of Pit Pattern analysis. Motivated by the shortcomings of discriminant classifier approaches, we present a generative model based strategy which is closely related to content-based image retrieval (CBIR)(More)
In this article, we discuss the discriminative power of a set of image features, extracted from subbands of the Gabor Wavelet Transform and the Dual-Tree Complex Wavelet Transform for the purpose computer-assisted zoom-endoscopy image classification. We incorporate color channel information into the classification process and show, that this leads to(More)
In this paper, we show that zoom-endoscopy images can be well classified according to the pit-pattern classification scheme by using texture-analysis methods in different wavelet domains. We base our approach on three different variants of the wavelet transform and propose that the color-channels of the RGB and LAB color model are an important source for(More)
In this work we present a method for an automated classification of en-doscopic images according to the pit pattern classification scheme. Images taken during colonoscopy are transformed using an extended and rotation invariant version of the Local Binary Patterns operator (LBP). The result of the transforms is then used to extract polygons from the images.(More)
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